Gait Performance as an Indicator of Cognitive Deficit in Older People
Bibliographic record
Abstract
Background: The purpose of this study was to analyze which gait variables are the best for detecting cognitive impairment and to determine if age and gender can influence gait variations in older people. Methods: 65 participants took part in this study (22 men and 43 women; age: 73.88 ± 9.56 years). We use the Montreal Cognitive Assessment (MoCA) to assess mild cognitive impairment (MCI). Gait speed (GS) and the complex gait test (CGT) were analyzed with photocells Witty (Microgate, Italia). The OptoGait system (Microgate, Italia) was used to analyze step length (SL) and step coefficient of variation (CV sl). Results: There was a significant association between MoCA and SL (r = 0.420; p = 0.002), CV sl (r = −0.591; p < 0.001), and CGT (r = −0.406; p = 0.001). Instrumental activities of daily living showed significant association with SL (r = 0.563; p < 0.001); CV sl (r = −0.762; p < 0.001), CGT (r = −0.622; p < 0.001), and GS (r = 0.418; p < 0.001). CV sl showed the best results with MoCA when linear regression analysis was applied (R2 = 0.560; p = 0.007; Y = 23.669 − 0.320x). Participants older than 79 years showed lower MoCA scores and poorer gait parameters than people younger than 79 years. Conclusions: CV sl, SL, CGT, and GS make it possible to detect MCI in older people, especially when these variables are evaluated as a whole.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".